Unverified50% confidenceFactExact time
The knowledge of large language models is encoded in a distributed manner across the parameter weight matrices, and the larger the number of parameters, the more long-tail knowledge can be retained.
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50%
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Long-term
Relevance
10/11/2026
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Related Claims
UnverifiedThe larger the parameter count, the stronger a model generally is in language understanding and generation quality, but the memory and compute requirements also increase proportionally.78% similarUnverifiedLLM的知识以分布式方式编码于数十亿参数的权重矩阵中，而非以符号化的逻辑规则存储78% similarVerifiedThe essence of large language models is learning the conditional probability distribution of what the next word is given the preceding text77% similarUnverifiedLarge models store parametric knowledge, with world knowledge compressed and encoded into billions of neural network weights rather than being stored in a structured knowledge base that can be precisely retrieved.77% similarUnverified模型微调能将知识永久写入模型权重，但需要大量标注数据、算力成本高、迭代周期长77% similar
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